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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "from sklearn.cluster import Birch\n",
    "from sklearn import preprocessing\n",
    "import glob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rt_invocations(faults_name):\n",
    "    # retrieve the response time of each invocation from data collection\n",
    "    # input: prefix of the csv file\n",
    "    # output: round-trip time\n",
    "    \n",
    "    latency_filename = faults_name + '_latency_source_90.csv'  # inbound\n",
    "    latency_df_source = pd.read_csv(latency_filename) \n",
    "    latency_df_source['unknown_front-end'] = 0\n",
    "    \n",
    "    latency_filename = faults_name + '_latency_source_90.csv' # outbound\n",
    "    latency_df_destination = pd.read_csv(latency_filename) \n",
    "    \n",
    "    latency_df = latency_df_destination.add(latency_df_source)    \n",
    "\n",
    "    return latency_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def birch_ad_with_smoothing(latency_df, threshold):\n",
    "    # anomaly detection on response time of service invocation. \n",
    "    # input: response times of service invocations, threshold for birch clustering\n",
    "    # output: anomalous service invocation\n",
    "    smoothing_window = 12\n",
    "    anomalies = []\n",
    "    #print('latency_df: ', latency_df)\n",
    "    for svc, latency in latency_df.iteritems():\n",
    "        # No anomaly detection in db\n",
    "        if svc != 'timestamp' and 'Unnamed' not in svc and 'rabbitmq' not in svc and 'db' not in svc:\n",
    "        # Dataframe Iterators gives data of each columns\n",
    "            #print(latency)\n",
    "            # Getting Rolling Mean\n",
    "            latency = latency.rolling(window=smoothing_window, min_periods=1).mean()\n",
    "\n",
    "            \n",
    "            x = np.array(latency) # Converting into numpy array\n",
    "            x = np.where(np.isnan(x), 0, x) # Changing Fields with NaN Values into 0\n",
    "            print('x', x)\n",
    "            normalized_x = preprocessing.normalize([x]) #\n",
    "\n",
    "            X = normalized_x.reshape(-1,1)\n",
    "\n",
    "#            threshold = 0.05\n",
    "\n",
    "            brc = Birch(branching_factor=50, n_clusters=None, threshold=threshold, compute_labels=True)\n",
    "            brc.fit(X)\n",
    "            brc.predict(X)\n",
    "\n",
    "            labels = brc.labels_\n",
    "#            centroids = brc.subcluster_centers_\n",
    "            n_clusters = np.unique(labels).size\n",
    "            if n_clusters > 1:\n",
    "                anomalies.append(svc)\n",
    "    return anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 0.55\n",
    "ad_threshold = 0.045\n",
    "\n",
    "\n",
    "# folders = ['1', '2', '3', '4', '5']\n",
    "folders = ['1']\n",
    "# faults_type = ['svc_latency', 'service_cpu', 'service_memory'] #, 'service_memory', 'svc_latency'\n",
    "faults_type = ['service_cpu']\n",
    "# targets = ['front-end', 'catalogue', 'orders', 'user', 'carts', 'payment', 'shipping']\n",
    "targets = ['carts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "target: carts  fault_type: service_cpu\nx [ 81.39344262  75.55783242  66.75562812  62.38829367  64.9541132\n  65.43524585  64.98345748 109.56365029 141.43753042 164.04658637\n 167.50535336 178.15441705 203.28339487 228.13987635 251.30435806\n 276.26109414 303.41745736 335.85571494 369.42914151 343.18261373\n 317.72311896 291.86701997 278.9344381  260.91835526 233.14859354\n 209.29519848 191.50144848 170.74718063 141.42924155 110.23317928\n  77.29728184]\nx [ 64.02777778  58.48128019  55.74808351  53.15408589  51.94216635\n  51.41402751  56.57956866  60.341944    70.50085599  68.48850624\n  68.6566273   67.06294732  65.51564725  68.75674512  70.22474821\n  71.8060593   77.9193974   87.4913166   86.69286972  87.56056452\n  79.16039764  80.13029518  81.49276703  92.58728362  99.17393554\n  99.33267885 100.49271673 100.25860302  94.10045908  87.16548433\n  87.91338759]\nx [196.875      171.51442308 248.41702279 263.50026709 250.80021368\n 241.96892806 238.97336691 269.39581369 288.99013227 305.76325019\n 317.32487985 330.7769732  351.49791618 380.78509567 381.24093612\n 389.29382074 408.73826518 427.9681759  448.03635771 440.23611262\n 433.44159974 411.90926823 399.99750352 388.57389241 371.97700246\n 346.93008888 348.90811085 345.29272624 345.01494846 344.68527813\n 339.36709631]\nx [48.92746114 56.40817501 53.92076532 55.80595861 54.99770806 53.89064504\n 57.19198147 59.86893315 63.8657557  64.60080176 64.56436523 63.10310715\n 63.09292787 64.83730618 65.21793681 67.81303691 71.12431142 72.55374014\n 72.72479277 73.65833708 71.931805   71.70479227 70.80207119 72.11781398\n 73.4673715  72.3651027  77.2477485  77.48375207 75.25386702 75.72654043\n 75.0605383 ]\nx [ 18.46153846  14.23076923  15.48717949  18.75824176  18.42398471\n  18.68665393  18.56177479  59.74155294  72.83424235  82.85081811\n  91.54482087  99.90566913 114.39284862 139.51189624 149.12300735\n 161.24822781 175.06941281 188.63001887 202.73592164 177.65258831\n 165.38238629 152.43123687 138.98416616 124.65232092 110.09096561\n  85.66032379  80.42421268  69.10553984  59.4854418   46.7165024\n  32.55479605]\nx [ 9.7826087   9.73001403  9.57945952 14.43459464 13.50767571 13.47861865\n 12.95061723 26.83179007 38.44307266 35.54491924 42.91962355 42.69715492\n 51.6546648  51.67321319 51.71595138 50.63261805 51.17958774 62.70005558\n 72.57928263 65.82928263 62.80150485 65.32256713 61.01701157 60.79379728\n 52.02107001 53.86273668 59.65394547 64.88311214 74.16050141 74.74861944\n 74.31058525]\nx [40.625      29.94212963 75.51697531 63.57523148 54.26732804 51.0561067\n 62.80999622 60.95874669 73.72803628 82.65523265 85.24213069 86.65713832\n 95.35505498 96.71678338 93.14840731 95.00257398 97.39245493 98.07102636\n 96.12658191 96.22745911 84.98726303 72.94758049 67.1049879  60.16735173\n 49.60125978 48.24570422 53.1751914  51.9564414  60.43858426 68.34334616\n 67.51001283]\nx [35.5        32.52272727 34.26397781 34.7158405  35.15113394 35.34132956\n 35.78416484 36.27705332 36.2740474  35.36851766 35.87657449 35.93778588\n 36.14985056 36.90524347 36.75840148 36.5200773  36.77800999 37.10781769\n 37.26105298 36.87901835 37.10818502 37.98709127 37.79394138 37.74435954\n 37.8268351  37.79772677 38.60490006 39.68121125 39.40910477 41.57217948\n 41.64042124]\nx [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n 0. 0. 0. 0. 0. 0. 0.]\n['front-end_carts', 'orders_carts', 'orders_payment', 'orders_shipping']\n"
     ]
    }
   ],
   "source": [
    "for folder in folders:\n",
    "    for fault_type in faults_type:\n",
    "        for target in targets:\n",
    "            if target == 'front-end' and fault_type != 'svc_latency':\n",
    "                #'skip front-end for service_cpu and service_memory'\n",
    "                continue \n",
    "            print('target:', target, ' fault_type:', fault_type)\n",
    "            \n",
    "            # prefix of csv files \n",
    "            # faults_name = '../faults/' + folder + '/' + fault_type + '_' + target\n",
    "            faults_name = './data/' + fault_type + '_' + target\n",
    "\n",
    "            latency_df = rt_invocations(faults_name)\n",
    "            \n",
    "            if (target == 'payment' or target  == 'shipping') and fault_type != 'svc_latency':\n",
    "                threshold = 0.02\n",
    "            else:\n",
    "                threshold = ad_threshold   \n",
    "            \n",
    "            # anomaly detection on response time of service invocation\n",
    "            anomalies = birch_ad_with_smoothing(latency_df, threshold)\n",
    "            print(anomalies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "latency = latency_df[['front-end_carts']]\n",
    "latency_mean = latency.rolling(window=12, min_periods=1).mean()\n",
    "x = latency_mean.to_numpy()\n",
    "x = np.where(np.isnan(x), 0, x) # Changing Fields with NaN Values into 0\n",
    "x = x.flatten()\n",
    "normalized_x = preprocessing.normalize([x]) #\n",
    "X = normalized_x.reshape(-1,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "brc = Birch(branching_factor=50, n_clusters=None, threshold=threshold, compute_labels=True)\n",
    "brc.fit(X)\n",
    "brc.predict(X)\n",
    "\n",
    "labels = brc.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "n_clusters = np.unique(labels).size\n",
    "n_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'d:\\\\THU Studies\\\\Advance Network Management\\\\Project\\\\MicroRCA'"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "path = os.path.dirname(os.getcwd())\n",
    "\n",
    "data = pd.read_csv(path + '\\\\training_data\\\\2020_05_04\\\\esb.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date'] = pd.to_datetime(data.startTime, unit='ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     serviceName      startTime  avg_time  num  succee_num  succee_rate  \\\n",
       "0        osb_001  1588521600000    0.5691  347         347          1.0   \n",
       "1        osb_001  1588521660000    0.5813  354         354          1.0   \n",
       "2        osb_001  1588521720000    0.5397  363         363          1.0   \n",
       "3        osb_001  1588521780000    0.6190  387         387          1.0   \n",
       "4        osb_001  1588521840000    0.4909  387         387          1.0   \n",
       "...          ...            ...       ...  ...         ...          ...   \n",
       "1434     osb_001  1588607700000    0.6240  600         600          1.0   \n",
       "1435     osb_001  1588607760000    0.5897  643         643          1.0   \n",
       "1436     osb_001  1588607820000    0.6495  619         619          1.0   \n",
       "1437     osb_001  1588607880000    0.6262  614         614          1.0   \n",
       "1438     osb_001  1588607940000    0.6530  592         592          1.0   \n",
       "\n",
       "                    date  \n",
       "0    2020-05-03 16:00:00  \n",
       "1    2020-05-03 16:01:00  \n",
       "2    2020-05-03 16:02:00  \n",
       "3    2020-05-03 16:03:00  \n",
       "4    2020-05-03 16:04:00  \n",
       "...                  ...  \n",
       "1434 2020-05-04 15:55:00  \n",
       "1435 2020-05-04 15:56:00  \n",
       "1436 2020-05-04 15:57:00  \n",
       "1437 2020-05-04 15:58:00  \n",
       "1438 2020-05-04 15:59:00  \n",
       "\n",
       "[1439 rows x 7 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>serviceName</th>\n      <th>startTime</th>\n      <th>avg_time</th>\n      <th>num</th>\n      <th>succee_num</th>\n      <th>succee_rate</th>\n      <th>date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>osb_001</td>\n      <td>1588521600000</td>\n      <td>0.5691</td>\n      <td>347</td>\n      <td>347</td>\n      <td>1.0</td>\n      <td>2020-05-03 16:00:00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>osb_001</td>\n      <td>1588521660000</td>\n      <td>0.5813</td>\n      <td>354</td>\n      <td>354</td>\n      <td>1.0</td>\n      <td>2020-05-03 16:01:00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>osb_001</td>\n      <td>1588521720000</td>\n      <td>0.5397</td>\n      <td>363</td>\n      <td>363</td>\n      <td>1.0</td>\n      <td>2020-05-03 16:02:00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>osb_001</td>\n      <td>1588521780000</td>\n      <td>0.6190</td>\n      <td>387</td>\n      <td>387</td>\n      <td>1.0</td>\n      <td>2020-05-03 16:03:00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>osb_001</td>\n      <td>1588521840000</td>\n      <td>0.4909</td>\n      <td>387</td>\n      <td>387</td>\n      <td>1.0</td>\n      <td>2020-05-03 16:04:00</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1434</th>\n      <td>osb_001</td>\n      <td>1588607700000</td>\n      <td>0.6240</td>\n      <td>600</td>\n      <td>600</td>\n      <td>1.0</td>\n      <td>2020-05-04 15:55:00</td>\n    </tr>\n    <tr>\n      <th>1435</th>\n      <td>osb_001</td>\n      <td>1588607760000</td>\n      <td>0.5897</td>\n      <td>643</td>\n      <td>643</td>\n      <td>1.0</td>\n      <td>2020-05-04 15:56:00</td>\n    </tr>\n    <tr>\n      <th>1436</th>\n      <td>osb_001</td>\n      <td>1588607820000</td>\n      <td>0.6495</td>\n      <td>619</td>\n      <td>619</td>\n      <td>1.0</td>\n      <td>2020-05-04 15:57:00</td>\n    </tr>\n    <tr>\n      <th>1437</th>\n      <td>osb_001</td>\n      <td>1588607880000</td>\n      <td>0.6262</td>\n      <td>614</td>\n      <td>614</td>\n      <td>1.0</td>\n      <td>2020-05-04 15:58:00</td>\n    </tr>\n    <tr>\n      <th>1438</th>\n      <td>osb_001</td>\n      <td>1588607940000</td>\n      <td>0.6530</td>\n      <td>592</td>\n      <td>592</td>\n      <td>1.0</td>\n      <td>2020-05-04 15:59:00</td>\n    </tr>\n  </tbody>\n</table>\n<p>1439 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.set_index(pd.DatetimeIndex(data['date']))\n",
    "df = df[['num','avg_time']]\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure (figsize=(20,8))\n",
    "plt.plot(df.index[:200],df.num[:200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.05003474635163308\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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140.629091 \r\nL 192.955649 169.448751 \r\nL 193.167159 159.720403 \r\nL 193.37867 161.867034 \r\nL 193.59018 125.054599 \r\nL 193.801691 157.84781 \r\nL 194.013201 164.150684 \r\nL 194.224712 156.61464 \r\nL 194.436222 144.41995 \r\nL 194.647733 146.840618 \r\nL 194.859244 167.43914 \r\nL 195.070754 141.314186 \r\nL 195.493775 165.61222 \r\nL 195.705286 159.172327 \r\nL 195.916796 156.888678 \r\nL 196.128307 172.874226 \r\nL 196.339817 179.542484 \r\nL 196.551328 151.818975 \r\nL 196.762838 160.58819 \r\nL 196.974349 174.290089 \r\nL 197.185859 178.537678 \r\nL 197.39737 144.968026 \r\nL 197.60888 28.73025 \r\nL 197.820391 170.636249 \r\nL 198.031901 178.400659 \r\nL 198.243412 174.6098 \r\nL 198.454922 176.299701 \r\nL 198.666433 205.210707 \r\nL 199.089454 159.400692 \r\nL 199.300964 151.727629 \r\nL 199.512475 188.722756 \r\nL 199.723985 179.314119 \r\nL 199.935496 190.184292 \r\nL 200.147006 193.06169 \r\nL 200.358517 183.470361 \r\nL 200.570028 199.501583 \r\nL 200.993049 168.124235 \r\nL 201.204559 191.600155 \r\nL 201.41607 148.758884 \r\nL 201.62758 171.184325 \r\nL 201.839091 164.333376 \r\nL 202.050601 186.758817 \r\nL 202.262112 185.160262 \r\nL 202.473622 139.213228 \r\nL 202.685133 140.081015 \r\nL 202.896643 162.506456 \r\nL 203.108154 171.138652 \r\nL 203.319664 152.093013 \r\nL 203.531175 178.72037 \r\nL 203.954196 174.701146 \r\nL 204.165706 177.487199 \r\nL 204.588727 145.927158 \r\nL 204.800238 156.705986 \r\nL 205.011748 157.162716 \r\nL 205.223259 171.321344 \r\nL 205.434769 158.258867 \r\nL 205.64628 175.431914 \r\nL 205.85779 169.448751 \r\nL 206.069301 172.737207 \r\nL 206.280812 149.535325 \r\nL 206.492322 151.955994 \r\nL 206.703833 156.020891 \r\nL 206.915343 176.025663 \r\nL 207.126854 146.840618 \r\nL 207.338364 187.946315 \r\nL 207.549875 190.549676 \r\nL 207.761385 158.669924 \r\nL 207.972896 183.013631 \r\nL 208.184406 169.403078 \r\nL 208.395917 178.72037 \r\nL 208.607427 143.826201 \r\nL 208.818938 160.451171 \r\nL 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226.585821 143.552163 \r\nL 226.797332 167.302121 \r\nL 227.008842 166.297315 \r\nL 227.220353 143.552163 \r\nL 227.431863 168.855003 \r\nL 227.643374 178.035275 \r\nL 228.066395 166.617026 \r\nL 228.277905 139.304574 \r\nL 228.489416 171.367017 \r\nL 228.700927 158.76127 \r\nL 228.912437 164.927125 \r\nL 229.123948 180.54729 \r\nL 229.335458 175.660279 \r\nL 229.546969 159.309346 \r\nL 229.758479 154.879066 \r\nL 229.96999 166.708372 \r\nL 230.1815 163.100205 \r\nL 230.393011 130.581032 \r\nL 230.604521 194.614572 \r\nL 231.027542 159.67473 \r\nL 231.239053 170.407884 \r\nL 231.450563 175.386241 \r\nL 231.873584 159.948768 \r\nL 232.085095 184.109783 \r\nL 232.296605 128.845458 \r\nL 232.508116 159.172327 \r\nL 232.719626 169.722789 \r\nL 232.931137 202.744366 \r\nL 233.142647 150.494458 \r\nL 233.565668 189.590543 \r\nL 233.988689 196.030435 \r\nL 234.2002 144.282931 \r\nL 234.411711 180.318925 \r\nL 234.623221 143.095433 \r\nL 234.834732 191.737174 \r\nL 235.046242 170.727595 \r\nL 235.257753 164.881452 \r\nL 235.469263 166.251642 \r\nL 235.680774 184.612186 \r\nL 235.892284 146.703599 \r\nL 236.103795 162.232418 \r\nL 236.315305 159.537711 \r\nL 236.526816 184.703532 \r\nL 236.738326 165.292509 \r\nL 236.949837 181.597768 \r\nL 237.161347 183.379015 \r\nL 237.372858 163.830973 \r\nL 237.584368 161.181939 \r\nL 237.795879 150.768496 \r\nL 238.007389 191.874193 \r\nL 238.2189 172.508842 \r\nL 238.43041 172.828553 \r\nL 238.641921 163.739627 \r\nL 238.853431 167.987216 \r\nL 239.064942 160.58819 \r\nL 239.487963 179.63383 \r\nL 239.699473 175.751625 \r\nL 239.910984 145.607447 \r\nL 240.122495 170.636249 \r\nL 240.334005 155.883872 \r\nL 240.545516 182.282863 \r\nL 240.757026 150.859842 \r\nL 240.968537 171.732401 \r\nL 241.391558 162.049726 \r\nL 241.603068 178.217967 \r\nL 241.814579 174.427108 \r\nL 242.2376 189.316505 \r\nL 242.44911 192.376596 \r\nL 242.660621 175.112203 \r\nL 242.872131 200.460716 \r\nL 243.083642 197.948701 \r\nL 243.295152 146.246869 \r\nL 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "k = np.percentile(data['avg_time'], 95)\n",
    "data['anomaly'] = data.avg_time>k\n",
    "print(sum(data.anomaly)/len(data.anomaly))\n",
    "plt.plot(data.startTime,data.avg_time)\n",
    "x = np.arange(8)*185\n",
    "# plt.vlines(data.startTime[x], 0.4, 1)\n",
    "plt.plot(data.startTime,-0.01*np.cos((data.startTime-data.startTime[0])/(data.startTime[185//6]-data.startTime[0]))+0.6, color = 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0       0.5691\n",
       "1       0.5813\n",
       "2       0.5397\n",
       "3       0.6190\n",
       "4       0.4909\n",
       "         ...  \n",
       "1434    0.6240\n",
       "1435    0.5897\n",
       "1436    0.6495\n",
       "1437    0.6262\n",
       "1438    0.6530\n",
       "Name: avg_time, Length: 1439, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "data['avg_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     serviceName      startTime  avg_time  num  succee_num  succee_rate  \\\n",
       "145      osb_001  1588530300000    0.7061  538         538          1.0   \n",
       "360      osb_001  1588543200000    0.9237  635         635          1.0   \n",
       "445      osb_001  1588548300000    0.7151  506         506          1.0   \n",
       "469      osb_001  1588549740000    0.7144  534         534          1.0   \n",
       "557      osb_001  1588555020000    0.7578  416         416          1.0   \n",
       "616      osb_001  1588558560000    0.7146  463         463          1.0   \n",
       "720      osb_001  1588564800000    0.8982  632         632          1.0   \n",
       "833      osb_001  1588571580000    0.7396  588         588          1.0   \n",
       "1027     osb_001  1588583220000    0.7448  573         573          1.0   \n",
       "1080     osb_001  1588586400000    0.9009  605         605          1.0   \n",
       "1109     osb_001  1588588140000    0.7217  506         506          1.0   \n",
       "1129     osb_001  1588589340000    0.7079  503         503          1.0   \n",
       "1369     osb_001  1588603800000    0.7055  607         607          1.0   \n",
       "1383     osb_001  1588604640000    0.7095  667         667          1.0   \n",
       "1433     osb_001  1588607640000    0.7065  642         642          1.0   \n",
       "\n",
       "                    date  anomaly  \n",
       "145  2020-05-03 18:25:00     True  \n",
       "360  2020-05-03 22:00:00     True  \n",
       "445  2020-05-03 23:25:00     True  \n",
       "469  2020-05-03 23:49:00     True  \n",
       "557  2020-05-04 01:17:00     True  \n",
       "616  2020-05-04 02:16:00     True  \n",
       "720  2020-05-04 04:00:00     True  \n",
       "833  2020-05-04 05:53:00     True  \n",
       "1027 2020-05-04 09:07:00     True  \n",
       "1080 2020-05-04 10:00:00     True  \n",
       "1109 2020-05-04 10:29:00     True  \n",
       "1129 2020-05-04 10:49:00     True  \n",
       "1369 2020-05-04 14:50:00     True  \n",
       "1383 2020-05-04 15:04:00     True  \n",
       "1433 2020-05-04 15:54:00     True  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>serviceName</th>\n      <th>startTime</th>\n      <th>avg_time</th>\n      <th>num</th>\n      <th>succee_num</th>\n      <th>succee_rate</th>\n      <th>date</th>\n      <th>anomaly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>145</th>\n      <td>osb_001</td>\n      <td>1588530300000</td>\n      <td>0.7061</td>\n      <td>538</td>\n      <td>538</td>\n      <td>1.0</td>\n      <td>2020-05-03 18:25:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>360</th>\n      <td>osb_001</td>\n      <td>1588543200000</td>\n      <td>0.9237</td>\n      <td>635</td>\n      <td>635</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:00:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>445</th>\n      <td>osb_001</td>\n      <td>1588548300000</td>\n      <td>0.7151</td>\n      <td>506</td>\n      <td>506</td>\n      <td>1.0</td>\n      <td>2020-05-03 23:25:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>469</th>\n      <td>osb_001</td>\n      <td>1588549740000</td>\n      <td>0.7144</td>\n      <td>534</td>\n      <td>534</td>\n      <td>1.0</td>\n      <td>2020-05-03 23:49:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>557</th>\n      <td>osb_001</td>\n      <td>1588555020000</td>\n      <td>0.7578</td>\n      <td>416</td>\n      <td>416</td>\n      <td>1.0</td>\n      <td>2020-05-04 01:17:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>616</th>\n      <td>osb_001</td>\n      <td>1588558560000</td>\n      <td>0.7146</td>\n      <td>463</td>\n      <td>463</td>\n      <td>1.0</td>\n      <td>2020-05-04 02:16:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>720</th>\n      <td>osb_001</td>\n      <td>1588564800000</td>\n      <td>0.8982</td>\n      <td>632</td>\n      <td>632</td>\n      <td>1.0</td>\n      <td>2020-05-04 04:00:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>833</th>\n      <td>osb_001</td>\n      <td>1588571580000</td>\n      <td>0.7396</td>\n      <td>588</td>\n      <td>588</td>\n      <td>1.0</td>\n      <td>2020-05-04 05:53:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td>osb_001</td>\n      <td>1588583220000</td>\n      <td>0.7448</td>\n      <td>573</td>\n      <td>573</td>\n      <td>1.0</td>\n      <td>2020-05-04 09:07:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1080</th>\n      <td>osb_001</td>\n      <td>1588586400000</td>\n      <td>0.9009</td>\n      <td>605</td>\n      <td>605</td>\n      <td>1.0</td>\n      <td>2020-05-04 10:00:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1109</th>\n      <td>osb_001</td>\n      <td>1588588140000</td>\n      <td>0.7217</td>\n      <td>506</td>\n      <td>506</td>\n      <td>1.0</td>\n      <td>2020-05-04 10:29:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1129</th>\n      <td>osb_001</td>\n      <td>1588589340000</td>\n      <td>0.7079</td>\n      <td>503</td>\n      <td>503</td>\n      <td>1.0</td>\n      <td>2020-05-04 10:49:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1369</th>\n      <td>osb_001</td>\n      <td>1588603800000</td>\n      <td>0.7055</td>\n      <td>607</td>\n      <td>607</td>\n      <td>1.0</td>\n      <td>2020-05-04 14:50:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1383</th>\n      <td>osb_001</td>\n      <td>1588604640000</td>\n      <td>0.7095</td>\n      <td>667</td>\n      <td>667</td>\n      <td>1.0</td>\n      <td>2020-05-04 15:04:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1433</th>\n      <td>osb_001</td>\n      <td>1588607640000</td>\n      <td>0.7065</td>\n      <td>642</td>\n      <td>642</td>\n      <td>1.0</td>\n      <td>2020-05-04 15:54:00</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "k = np.percentile(data['avg_time'], 99)\n",
    "data[data['avg_time']>k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Int64Index([ 145,  360,  445,  469,  557,  616,  720,  833, 1027, 1080, 1109,\n            1129, 1369, 1383, 1433],\n           dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "print(data[data['avg_time']>k].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    serviceName      startTime  avg_time  num  succee_num  succee_rate  \\\n",
       "350     osb_001  1588542600000    0.5875  620         620          1.0   \n",
       "351     osb_001  1588542660000    0.6642  630         630          1.0   \n",
       "352     osb_001  1588542720000    0.6357  626         626          1.0   \n",
       "353     osb_001  1588542780000    0.6363  643         643          1.0   \n",
       "354     osb_001  1588542840000    0.6032  633         633          1.0   \n",
       "355     osb_001  1588542900000    0.6386  649         649          1.0   \n",
       "356     osb_001  1588542960000    0.6222  594         594          1.0   \n",
       "357     osb_001  1588543020000    0.6333  613         613          1.0   \n",
       "358     osb_001  1588543080000    0.6669  653         653          1.0   \n",
       "359     osb_001  1588543140000    0.6237  648         648          1.0   \n",
       "360     osb_001  1588543200000    0.9237  635         635          1.0   \n",
       "361     osb_001  1588543260000    0.5847  303         303          1.0   \n",
       "362     osb_001  1588543320000    0.5694  331         331          1.0   \n",
       "363     osb_001  1588543380000    0.5831  382         382          1.0   \n",
       "364     osb_001  1588543440000    0.6050  370         370          1.0   \n",
       "365     osb_001  1588543500000    0.5232  359         359          1.0   \n",
       "366     osb_001  1588543560000    0.5899  322         322          1.0   \n",
       "367     osb_001  1588543620000    0.5429  405         405          1.0   \n",
       "368     osb_001  1588543680000    0.5566  355         355          1.0   \n",
       "369     osb_001  1588543740000    0.5351  359         359          1.0   \n",
       "\n",
       "                   date  anomaly  \n",
       "350 2020-05-03 21:50:00    False  \n",
       "351 2020-05-03 21:51:00    False  \n",
       "352 2020-05-03 21:52:00    False  \n",
       "353 2020-05-03 21:53:00    False  \n",
       "354 2020-05-03 21:54:00    False  \n",
       "355 2020-05-03 21:55:00    False  \n",
       "356 2020-05-03 21:56:00    False  \n",
       "357 2020-05-03 21:57:00    False  \n",
       "358 2020-05-03 21:58:00    False  \n",
       "359 2020-05-03 21:59:00    False  \n",
       "360 2020-05-03 22:00:00     True  \n",
       "361 2020-05-03 22:01:00    False  \n",
       "362 2020-05-03 22:02:00    False  \n",
       "363 2020-05-03 22:03:00    False  \n",
       "364 2020-05-03 22:04:00    False  \n",
       "365 2020-05-03 22:05:00    False  \n",
       "366 2020-05-03 22:06:00    False  \n",
       "367 2020-05-03 22:07:00    False  \n",
       "368 2020-05-03 22:08:00    False  \n",
       "369 2020-05-03 22:09:00    False  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>serviceName</th>\n      <th>startTime</th>\n      <th>avg_time</th>\n      <th>num</th>\n      <th>succee_num</th>\n      <th>succee_rate</th>\n      <th>date</th>\n      <th>anomaly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>350</th>\n      <td>osb_001</td>\n      <td>1588542600000</td>\n      <td>0.5875</td>\n      <td>620</td>\n      <td>620</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:50:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>351</th>\n      <td>osb_001</td>\n      <td>1588542660000</td>\n      <td>0.6642</td>\n      <td>630</td>\n      <td>630</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:51:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>352</th>\n      <td>osb_001</td>\n      <td>1588542720000</td>\n      <td>0.6357</td>\n      <td>626</td>\n      <td>626</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:52:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>353</th>\n      <td>osb_001</td>\n      <td>1588542780000</td>\n      <td>0.6363</td>\n      <td>643</td>\n      <td>643</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:53:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>354</th>\n      <td>osb_001</td>\n      <td>1588542840000</td>\n      <td>0.6032</td>\n      <td>633</td>\n      <td>633</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:54:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>355</th>\n      <td>osb_001</td>\n      <td>1588542900000</td>\n      <td>0.6386</td>\n      <td>649</td>\n      <td>649</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:55:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>356</th>\n      <td>osb_001</td>\n      <td>1588542960000</td>\n      <td>0.6222</td>\n      <td>594</td>\n      <td>594</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:56:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>357</th>\n      <td>osb_001</td>\n      <td>1588543020000</td>\n      <td>0.6333</td>\n      <td>613</td>\n      <td>613</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:57:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>358</th>\n      <td>osb_001</td>\n      <td>1588543080000</td>\n      <td>0.6669</td>\n      <td>653</td>\n      <td>653</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:58:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>359</th>\n      <td>osb_001</td>\n      <td>1588543140000</td>\n      <td>0.6237</td>\n      <td>648</td>\n      <td>648</td>\n      <td>1.0</td>\n      <td>2020-05-03 21:59:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>360</th>\n      <td>osb_001</td>\n      <td>1588543200000</td>\n      <td>0.9237</td>\n      <td>635</td>\n      <td>635</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:00:00</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>361</th>\n      <td>osb_001</td>\n      <td>1588543260000</td>\n      <td>0.5847</td>\n      <td>303</td>\n      <td>303</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:01:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>362</th>\n      <td>osb_001</td>\n      <td>1588543320000</td>\n      <td>0.5694</td>\n      <td>331</td>\n      <td>331</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:02:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>363</th>\n      <td>osb_001</td>\n      <td>1588543380000</td>\n      <td>0.5831</td>\n      <td>382</td>\n      <td>382</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:03:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>364</th>\n      <td>osb_001</td>\n      <td>1588543440000</td>\n      <td>0.6050</td>\n      <td>370</td>\n      <td>370</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:04:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>365</th>\n      <td>osb_001</td>\n      <td>1588543500000</td>\n      <td>0.5232</td>\n      <td>359</td>\n      <td>359</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:05:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>366</th>\n      <td>osb_001</td>\n      <td>1588543560000</td>\n      <td>0.5899</td>\n      <td>322</td>\n      <td>322</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:06:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>367</th>\n      <td>osb_001</td>\n      <td>1588543620000</td>\n      <td>0.5429</td>\n      <td>405</td>\n      <td>405</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:07:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>368</th>\n      <td>osb_001</td>\n      <td>1588543680000</td>\n      <td>0.5566</td>\n      <td>355</td>\n      <td>355</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:08:00</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>369</th>\n      <td>osb_001</td>\n      <td>1588543740000</td>\n      <td>0.5351</td>\n      <td>359</td>\n      <td>359</td>\n      <td>1.0</td>\n      <td>2020-05-03 22:09:00</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "data[350:370]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}